Fraud Resistant Biometric Financial Transaction System and Method

ABSTRACT

A method and system for authenticating financial transactions is disclosed wherein biometric data is acquired from a person and the probability of liveness of the person and probability of a match between the person or token and known biometric or token information are calculated, preferably according to a formula D=P(p)*(K+P(m)), wherein K is a number between 0.1 and 100, and authenticating if the value of D exceeds a predetermined value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application60/827,738, filed Oct. 2, 2006, which is hereby incorporated byreference.

BACKGROUND OF THE INVENTION

This invention relates to biometric identification and authenticationsystems and methods, more particularly to authentication for financialtransactions using biometrics.

Biometric identification and authentication systems are known in theart, for example systems to compare facial features, iris imagery,fingerprints, finger vein images, and palm vein images have been used.Such systems are known to be useful for either comparing biometric dataacquired from an individual to stored sets of biometric data of known“enrolled” individuals, or to compare biometric data acquired from anindividual to a proposed template such as when an identification card issupplied to the system by the individual.

Turk, et al., U.S. Pat. No. 5,164,992, discloses a recognition systemfor identifying members of an audience, the system including an imagingsystem which generates an image of the audience; a selector module forselecting a portion of the generated image; a detection means whichanalyzes the selected image portion to determine whether an image of aperson is present; and a recognition module responsive to the detectionmeans for determining whether a detected image of a person identified bythe detection means resembles one of a reference set of images ofindividuals. If the computed distance is sufficiently close to facespace (i.e., less than the pre-selected threshold), recognition module10 treats it as a face image and proceeds with determining whose face itis (step 206). This involves computing distances between the projectionof the input image onto face space and each of the reference face imagesin face space. If the projected input image is sufficiently close to anyone of the reference faces (i.e., the computed distance in face space isless than a predetermined distance), recognition module 10 identifiesthe input image as belonging to the individual associated with thatreference face. If the projected input image is not sufficiently closeto any one of the reference faces, recognition module 10 reports that aperson has been located but the identity of the person is unknown.

Daugman, U.S. Pat. No. 5,291,560, disclosed a method of uniquelyidentifying a particular human being by biometric analysis of the irisof the eye.

Yu, et al., U.S. Pat. No. 5,930,804, discloses a Web-basedauthentication system and method, the system comprising at least one Webclient station, at least one Web server station and an authenticationcenter. The Web client station is linked to a Web cloud, and providesselected biometric data of an individual who is using the Web clientstation. The Web server station is also linked to the Web cloud. Theauthentication center is linked to at least one of the Web client andWeb server stations so as to receive the biometric data. Theauthentication center, having records of one or more enrolledindividuals, provides for comparison of the provided data with selectedrecords. The method comprises the steps of (i) establishing parametersassociated with selected biometric characteristics to be used inauthentication; (ii) acquiring, at the Web client station, biometricdata in accordance with the parameters; (iii) receiving, at anauthentication center, a message that includes biometric data; (iv)selecting, at the authentication center, one or more records from amongrecords associated with one or more enrolled individuals; and (v)comparing the received data with selected records. The comparisons ofthe system and method are to determine whether the so-compared live datasufficiently matches the selected records so as to authenticate theindividual seeking access of the Web server station, which access istypically to information, services and other resources provided by oneor more application servers associated with the Web server station. Ifthe computed distance is sufficiently close to face space (i.e., lessthan the pre-selected threshold), recognition module 10 treats it as aface image and proceeds with determining whose face it is (step 206).This involves computing distances between the projection of the inputimage onto face space and each of the reference face images in facespace. If the projected input image is sufficiently close to any one ofthe reference faces (i.e., the computed distance in face space is lessthan a predetermined distance), recognition module 10 identifies theinput image as belonging to the individual associated with thatreference face. If the projected input image is not sufficiently closeto any one of the reference faces, recognition module 10 reports that aperson has been located but the identity of the person is unknown.

Different biometrics perform differently. For example, the facebiometric is easy to acquire (a web camera for example) but it's abilityto tell an impostor from an authentic person is somewhat limiting. Infact in most biometrics a threshold must be set which trades off howmany impostors are incorrectly accepted versus how many true authenticsare rejected. For example, if a threshold is set at 0 (figuratively),then no authentics would be rejected, but every impostor will also beaccepted. If the threshold is set at 1 (again figuratively), noimpostors will get through but neither will any authentics. If thethreshold is set at 0.5 (again figuratively), then a fraction ofimpostors will get through and a fraction of authentics will not getthrough. Even though some biometrics such as the iris are sufficientlyaccurate to have no cross-over between the authentics and impostordistributions when the iris image quality is good, if the iris image ispoor then there will be a cross-over and the problem reoccurs.

In the field of authentication of financial transactions, most systemsare designed to compare biometric data from an individual to a knowntemplate rather than to a set of enrolled individuals.

However, in the field of authentication of financial transactions, highlevels of accuracy and speed are critical. For example, to authenticatea banking transaction, there is high motivation for an imposter to tryto spoof the system and yet the financial institution would require afast authentication process and a low rate of false rejects or denials.In this field, even a small percentage of rejections of authentics canresult in an enormous number of unhappy customers, simply because of thehuge number of transactions. This has prevented banks from using certainbiometrics.

In addition, informing the customer (or attempted fraudster) that theysuccessfully got through a biometric system (or not) is not desirablebecause it enables fraudsters to obtain feedback on methods for tryingto defeat the system. Also, there is little or no deterrent for anattempted fraudster to keep on attempting to perform a fraudulenttransaction.

One problem faced by biometric recognition systems involves thepossibility of spoofing. For example, a life-sized, high-resolutionphotograph of a person may be presented to an iris recognition system.The iris recognition systems may capture an image of this photograph andgenerate a positive identification. This type of spoofing presents anobvious security concern for the implementation of an iris recognitionsystem. One method of addressing this problem has been to shine a lightonto the eye, then increase or decrease the intensity of the light. Alive, human eye will respond by dilating the pupil. This dilation isused to determine whether the iris presented for recognition is a live,human eye or merely a photograph—since the size of a pupil on aphotograph obviously will not change in response to changes in theintensity of light.

In biometric recognition systems using fingerprint, finger vein, palmvein, or other imagery, other methods of determining whether spoofing isbeing attempted use temperature or other measures of liveness, the termliveness being used herein for any step or steps taken to determinewhether the biometric data is being acquired from a live human ratherthan a fake due to a spoof attempt. More specifically however, in thisinvention, we define probability of liveness as the probability thatbiometric data has been acquired that can be used by an automatic ormanual method to identify the user.

In prior biometric systems which include means and steps to determineliveness, the liveness test is conducted or carried out first, prior tothe match process or matching module.

More specifically, in the prior art the decision to authorize atransaction does not separately consider a measure of liveness and ameasure of match. By match step or module, we mean the steps and systemcomponents which function to calculate the probability of a matchbetween acquired biometric data from an individual or purportedindividual being authenticated and data acquired from known individuals.

The prior systems and methods have not achieved significant commercialsuccess in the field of authenticating financial transactions due, inpart, from the insufficient speed and accuracy from which priorbiometric authentication systems for financial transactions suffered.More specifically, the current methods of basing a decision to perform afinancial transaction on the measure of match means that many validcustomers are rejected, due to the finite false reject rate. There istherefore a need in this field of biometric authentication systems andmethods for financial transactions for improved deterrent againstattempted fraudulent transactions, and decreased rejection of validcustomers.

SUMMARY OF THE INVENTION

These needs and others as will become apparent from the followingdescription and drawings, are achieved by the present invention whichcomprises in one aspect a system for

In another aspect, the invention comprises a method of authenticatingfinancial transactions comprising acquiring biometric data from aperson, calculating probability of liveness, Pp, of the person andprobability of a match, Pm, between the person and known biometricinformation, and providing an authenticating decision, D, based on acombination of Pp and Pm. In certain embodiments an authenticationdecision, D, is calculated as a function of the probability of a matchPm and the probability of a live person, Pp, according to the formulaD=Pp*(K+Pm), wherein K is a number between 0.1 and 100, and in someembodiments K is a number between 0.5 and 1.5.

In some embodiments a first image is presented on a computer screen,wherein the computer screen is oriented to face a user; at least onecamera is positioned proximate the computer screen, wherein the at leastone camera is oriented to face the user so that light emitted by thecomputer screen as the first image is reflected by the user and capturedby the at least one camera; obtaining a second image through the atleast one camera; and determining whether at least a portion of thesecond image includes a representation of the first image on thecomputer screen reflected by a curved surface consistent with a humaneye.

In certain embodiments the probability of a live person, Pp, iscalculated by presenting a first image on a computer screen positionedin front of a user; capturing a first reflection of the first image offof the user through a camera; presenting a second image on the computerscreen positioned in front of the user; capturing a second reflection ofthe second image off of the user through the camera; comparing the firstreflection of the first image with the second reflection of the secondimage to determine whether the first reflection and the secondreflection were formed by a curved surface consistent with a human eye.

Alternatively wherein the probability of a live person, Pp, can becalculated by obtaining a first image of a user positioned in front of acomputer screen from a first perspective; obtaining a second image ofthe user positioned in front of the computer screen from a secondperspective; identifying a first portion of the first image and a secondportion of the second image containing a representation of a human eye;and detecting a human eye when the first portion of the first imagediffers from the second portion of the second image.

The probability of a live person, Pp, is calculated in other embodimentsby measuring finger or palm temperature and comparing the resultantmeasured temperature to expected temperature for a human.

The probability of a match, Pm, can be calculated in any way which isdesired, for example by iris recognition, fingerprint image recognition,finger vein image recognition, or palm vein image recognition.

Another aspect of the invention is a system for carrying out the method.

A still further aspect and an advantage of the invention is that if aperson fails or passes authentication, the person is not informed as towhether non-authentication or authentication was based on probability ofliveliness or probability of matching of biometric image. This makes itmuch more difficult for an attempted fraudster to refine theirfraudulent methods since they are not being provided clear feedback.

As compared to conventional biometric systems and methods, the inventiondoes not merely depend on the probability that the person is who theysaid they are when authorizing a transaction. The invention includescalculating a second probability which is the probability that thebiometric data is from a real person in the first place. The firstprobability is determined using any biometric algorithm. The secondprobability is determined using other algorithms which determine whetherthe biometric data or the person from whom the data is collected is areal person. The decision to authorize a transaction is now a functionof both these probabilities. Often, if the first probability is high (agood match), then the second probability typically will also be high (areal person). However, in some cases where a good customer is trying toperform a transaction and the biometric algorithm is having difficultyperforming a match (because light is limited for example and theperson's web-cam has a low-contrast image), then the first probabilitycould be low but the second probability could still be high.

The algorithms to determine the second probability (confidence inwhether a person is real or not) can be designed to be in many casesless sensitive to conditions out of the control of the algorithms, suchas illumination changes and orientation of the person, compared toalgorithms that compute the first probability (confidence that theperson is a particular person) which are often very sensitive toillumination changes and orientation of the person. Because of this, andsince we combine the 2 probabilities to make a decision in atransaction, the reject rate of true authentics can be designed to begreatly reduced.

The invention authorizes transactions based on a combination of the twoprobabilities, an attempted fraudster is never sure whether atransaction was authorized or not authorized because they were matchedor not matched, or because they were or were not detected as a realperson and eliminates the clear feedback that criminals are providedtoday that they use to develop new methods to defeat systems. As abi-product, the invention provides an enormous deterrent to criminalssince the system is acquiring biometric data that they have no idea canor cannot be used successfully as evidence against them. Even if thereis a small probability that evidence can be used against them issufficient for many criminals to not perform fraud, in consideration ofthe consequences of the charges and the damming evidence of biometricdata (such as a picture of a face tied to a transaction). An analogy tothis latter point is CCTV cameras in a high street, which typicallyreduces crime substantially since people are aware that there is apossibility they will be caught on camera.

A preferred formula used in calculating a decision whether toauthenticate a transaction is D=P(p)*(1+P(m)), where D is the decisionprobability, P(m) is the probability of a match with a range of 0 to 1,and P(p) is the probability the person is real and the biometric data isvalid from 0 to 1. If the algorithm detects person is not live, and nomatch detected: D=0*(1+0)=0. If the algorithm detects strongly that theperson is live, and yet no match is detected: D=1*(1+0)=1. If thealgorithm detects strongly that the person is live, and a very goodmatch is detected: D=1*(1+1)=2. If the algorithm detects strongly thatthe person is live (or more specifically, that biometric data has beencollected that can be used by a manual or automatic methodafter-the-fact to identify the person in prosecution for example), and apoor match is detected of 0.3: D=1*(1+0.3)=1.3 If the threshold is setat, for example, 1.2 for D, then essentially in the latter case, thetransaction will be authorized even though the biometric match is nothigh. This is because the system determined that the biometric datacollected can be used by a manual or automatic method after-the-fact toidentify the person in prosecution for example. A higher transaction maybe authorized if the value of D is higher. Many other functions of Ppand Pm can be used. We use the parallel result to authorize atransaction or access control or other permission, where rejection of atrue customer has significant penalty such as a loss of a customer. Inthe prior art, false rejects and true accepts are often addressed onlyin consideration of the biometric match performance, and the substantialbusiness consequences of a false reject is often not considered, andtherefore few systems have been implemented practically.

A special advantage of this method and system is that by combining inone algorithm the live-person result with the match result, a fraudulentuser does not know whether he or she was authorized or declined as aresult of a bad or good match, or because the system has capturedexcellent live-person data that can be used for prosecution or at leastembarrassing public disclosure. The system results in a large deterrentsince in the process of trying to defeat a system, the fraudulent userwill have to present some live-person data to the system and they willnot know how much or how little live-person data is required toincriminate themselves. The fraudulent user is also not able todetermine precisely how well their fraudulent methods are working, whichtakes away the single most important tool of a fraudster, i.e., feedbackon how well their methods are working. At best, they get feedback on thecombination of live-person results and match results, but not on eitherindividually. For example, a transaction may be authorized because theprobability of a live-person is very high, even if the match probabilityis low. The invention collects a set of live-person data that can beused to compile a database or watch list of people who attempt toperform fraudulent transactions, and this can be used to recognizefraudsters at other transactions such as check-cashing for example byusing a camera and another face recognition system. The system alsoensures that some live-person data is captured, then it provides a meansto perform customer redress (for example, if a customer complains thenthe system can show the customer a picture of them performing atransaction, or a bank agent can manually look at the picture of theuser performing the transaction and compare it with a record of the useron file).

The biometric data gathered for calculating Pp can be stored and usedlater for manual verification or automatic checking.

In the prior art, only Pm has been involved in the decision metric.According to the present invention, Pp is combined so that for a givenPm, the decision criteria, D, is moved toward acceptance compared towhen only Pm is involved if Pp is near 1, so that if the system hasacquired good biometric data with sufficient quality for potentialprosecution and manual or automatic biometric matching, then it is morelikely to accept a match based on given biometric data used to calculatePm, thereby moving the performance of a transaction system for authenticusers from 98 percent to virtually 100 percent while still gatheringdata which can be used for prosecution or deterrent.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an authentication system according to theinvention.

DETAILED DESCRIPTION

Referring first to FIG. 1, the overall process is to compute 11 theprobability, Pp, of a live person being presented, compute 13 theprobability of a biometric match, Pm, computing 14 D according to theaforementioned formula, wherein at decision block 15 if D exceeds apreset threshold, the transaction is authorized 17 or, if D does notexceed the preset threshold, the transaction is not authorized, 16.

Referring now to FIG. 2, an example of a system and method of obtainingdata used for calculating the probability of a live person 21 is shown.First, an image is displayed on a screen 23 with a black bar 24 on theright and a white area 25 on the left, and an image from a web-camera 26that the person 21 looks at is recorded. A second image is displayed onthe screen (not shown), but this time the black bar is on the left andthe white area is on the right and a second image from the web-camera 26is recorded.

The difference between the two images is recorded and the difference ateach pixel is squared. The images are then blurred by convolving with alow-pass filter and then threshold the image. Areas above threshold areareas of change between the two images. The system expects to see achange primarily on the cornea, where a sharp image of the screen isreflected.

Referring to FIGS. 3 and 4 which represent cornea C with pupil P andsection S1 at time T1 and S2 at time T2, with I representing an iris,given the curved geometry of the cornea, for a live curved andreflective cornea, the black and white area should have a particularcurved shape—specifically a curved black bar and a curved white area(much like a fish-eye lens view). A template of the expected view iscorrelated with the first image obtained on the web-camera only in theregion of the eye as detected by the prior step), and the peak value ofthe correlation is detected. The process is then repeated with thetemplate expected from the second image.

The minimum of the two correlation scores (which will lie between −1to 1) is correlated and normalized it to be between 0 and 1 by adding 1and dividing by 2. This is the probability of measure of liveness=P(p).

Using the method described in Turk, et al., U.S. Pat. No. 5,164,992, aface recognition match score, Pm, is calculated and then normalized tobe between 0 and 1.

The system then computes D=(P(L)*(1+P(M))/2. If P(L) ranges from 0 to 1,and P(M) ranges from 0 to 1, then D ranges from 0 to 1. A threshold of0.55 is set. If the value of D for a particular transaction/customer isabove 0.55, then the transaction authenticated and allowed to proceed.If the value of D is less than or equal to 0.55, then authenticationfails and the transaction is not allowed to proceed. If P(L)=0.95 (high)and P(M)=0.95 ((high), then D=0.95, which is well above thethreshold—the transaction goes through as expected. If P(L)=0.95 (high),but P(M)=0.25 (poor), then D=0.6, and the transaction still goesthrough.

The present invention, therefore, is well adapted to carry out theobjects and attain the ends and advantages mentioned, as well as othersinherent therein. While the invention has been depicted and describedand is defined by reference to particular preferred embodiments of theinvention, such references do not imply a limitation on the invention,and no such limitation is to be inferred. The invention is capable ofconsiderable modification, alteration and equivalents in form andfunction, as will occur to those ordinarily skilled in the pertinentarts. The depicted and described preferred embodiments of the inventionare exemplary only and are not exhaustive of the scope of the invention.Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

1. A method of authenticating financial transactions comprisingacquiring biometric data from a person, calculating probability ofliveness, Pp, of the person and probability of a match, Pm, between theperson or token and known biometric or token information, and providingan authentication decision, D, based on a combination of Pp and Pm. 2.The method of claim 1 wherein an D is calculated as a function of Pm andPp according to the formula D=P(p)*(K+P(m)), wherein K is a numberbetween 0.1 and
 100. 3. The method of claim 1 wherein an authenticationdecision, D, is calculated as a function of Pm and Pp according to theformula D=P(p)*(K+P(m)), wherein K is a number between 0.5 and 1.5. 4.The method of claim 1 wherein the biometric data is stored and is usedfor manual verification.
 5. The method of claim 1 wherein Pp isdetermined by steps comprising presenting a first image on a computerscreen, wherein the computer screen is oriented to face a user;positioning at least one camera proximate the computer screen, whereinthe at least one camera is oriented to face the user so that lightemitted by the computer screen as the first image is reflected by theuser and captured by the at least one camera; obtaining a second imagethrough the at least one camera; and determining whether at least aportion of the second image includes a representation of the first imageon the computer screen reflected by a curved surface consistent with ahuman eye.
 6. The method of claim 1 wherein Pp is calculated by stepscomprising presenting a first image on a computer screen positioned infront of a user; capturing a first reflection of the first image off ofthe user through a camera; presenting a second image on the computerscreen positioned in front of the user; capturing a second reflection ofthe second image off of the user through the camera; comparing the firstreflection of the first image with the second reflection of the secondimage to determine whether the first reflection and the secondreflection were formed by a curved surface consistent with a human eye.7. The method of claim 1 wherein Pp is calculated by steps comprisingobtaining a first image of a user positioned in front of a computerscreen from a first perspective; obtaining a second image of the userpositioned in front of the computer screen from a second perspective;identifying a first portion of the first image and a second portion ofthe second image containing a representation of a human eye; anddetecting a human eye when the first portion of the first image differsfrom the second portion of the second image.
 8. The method of claim 1wherein Pp is calculated by steps comprising presenting one or moreilluminators, wherein the illuminators are oriented to face a user;positioning at least one camera proximate the illuminators, wherein theat least one camera is oriented to face the user so that light emittedby the illuminators is reflected by the user and captured by the atleast one camera as a first image; obtaining a second image through theat least one camera at a different time than the first image; detectinga first position of a reflection in the first image and a secondposition of a reflection in the second image; normalizing a positionalchange of the user in the first image and the second image based uponthe first position and the second position, wherein the step ofnormalizing comprises compensating for motion during the time betweenthe first image and the second image by using at least a translationmotion model to detect residual motion of the position of thereflection; and determining whether a change between at least a portionof the first image and the second image is consistent with reflection bya curved surface consistent with that of a human eye.
 9. The method ofclaim 1 wherein Pp is calculated by steps comprising measuring finger orpalm temperature and comparing the resultant measured temperature toexpected temperature for a human.
 10. The method of claim 1 wherein Pmis calculated by steps comprising iris recognition, fingerprint imagerecognition, finger vein image recognition, or palm vein imagerecognition, face recognition, or token match.
 11. The method of claim 1wherein if a person fails authentication, the person is not informed asto whether non-authentication was based on probability of non-livelinessor probability of non-matching of biometric image.
 12. The method ofclaim 1 wherein the accuracy of authentication of an authentic person isimproved use of Pp.
 13. The method of claim 1 wherein biometric dataused to calculate Pp is obtained from a web camera connected to acomputer.
 14. The method of claim 1 wherein biometric data used tocalculate Pp is obtained from a cell phone camera.
 15. A method ofauthenticating financial transactions comprising acquiring biometricdata from a person, calculating probability of liveness, Pp, of theperson and probability of a match, Pm, between the person or a token andknown biometric or token information, and providing and authenticationdecision, D, based on a combination of Pp and Pm.
 16. A system forauthenticating financial transactions comprising means to acquirebiometric data from a person and calculate probability of liveness, Pp,of the person and probability of a match, Pm, between the person ortoken and known biometric or token information.
 17. The system of claim167 wherein the means to calculate comprises a computer programmed todetermine Pp, Pm, and to authenticate if the value of D exceeds apredetermined value according to a formula D=P(p)*(K+P(m)) , wherein Kis a number between 0.1 and
 100. 18. The system of claim 16 wherein themeans to calculate comprises a computer programmed to determine Pp, Pm,and to authenticate if the value of D exceeds a predetermined valueaccording to a formula D=P(p)*(K+P(m)), wherein K is a number between0.5 and 1.5.